Note
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Within Session SSVEP#
This Example shows how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using a CCA pipeline.
The within-session evaluation assesses the performance of a classification pipeline using a 5-fold cross-validation. The reported metric (here, accuracy) is the average of all fold.
# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
#
# License: BSD (3-clause)
import warnings
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.pipeline import make_pipeline
import moabb
from moabb.datasets import Kalunga2016
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import SSVEP
from moabb.pipelines import SSVEP_CCA
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=RuntimeWarning)
moabb.set_log_level("info")
Loading Dataset#
Load 2 subjects of Kalunga2016 dataset
subj = [1, 3]
dataset = Kalunga2016()
dataset.subject_list = subj
Choose Paradigm#
We select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on the data and we keep only the first 3 classes, that is stimulation frequency of 13Hz, 17Hz and 21Hz.
Create Pipelines#
Use a Canonical Correlation Analysis classifier
interval = dataset.interval
freqs = paradigm.used_events(dataset)
pipeline = {}
pipeline["CCA"] = make_pipeline(SSVEP_CCA(n_harmonics=3))
Get Data (optional)#
To get access to the EEG signals downloaded from the dataset, you could use dataset.get_data(subjects=[subject_id]) to obtain the EEG under MNE format, stored in a dictionary of sessions and runs. Otherwise, paradigm.get_data(dataset=dataset, subjects=[subject_id]) allows to obtain the EEG data in scikit format, the labels and the meta information. In paradigm.get_data, the EEG are preprocessed according to the paradigm requirement.
# sessions = dataset.get_data(subjects=[3])
# X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3])
Evaluation#
The evaluation will return a DataFrame containing a single AUC score for each subject and pipeline.
overwrite = True # set to True if we want to overwrite cached results
evaluation = WithinSessionEvaluation(
paradigm=paradigm, datasets=dataset, suffix="examples", overwrite=overwrite
)
results = evaluation.process(pipeline)
print(results.head())
Kalunga2016-WithinSession: 0%| | 0/2 [00:00<?, ?it/s]
Kalunga2016-WithinSession: 50%|█████ | 1/2 [00:09<00:09, 9.55s/it]
0%| | 0.00/2.27M [00:00<?, ?B/s]
0%|▏ | 11.3k/2.27M [00:00<00:25, 90.3kB/s]
4%|█▍ | 86.0k/2.27M [00:00<00:07, 294kB/s]
14%|█████▍ | 315k/2.27M [00:00<00:02, 858kB/s]
38%|██████████████▎ | 855k/2.27M [00:00<00:00, 1.91MB/s]
87%|████████████████████████████████ | 1.97M/2.27M [00:00<00:00, 4.41MB/s]
0%| | 0.00/2.27M [00:00<?, ?B/s]
100%|█████████████████████████████████████| 2.27M/2.27M [00:00<00:00, 10.5GB/s]
0%| | 0.00/2.13M [00:00<?, ?B/s]
1%|▏ | 13.3k/2.13M [00:00<00:20, 102kB/s]
4%|█▋ | 94.2k/2.13M [00:00<00:06, 316kB/s]
11%|████▍ | 241k/2.13M [00:00<00:03, 531kB/s]
19%|███████▍ | 404k/2.13M [00:00<00:02, 659kB/s]
29%|███████████▎ | 616k/2.13M [00:00<00:01, 817kB/s]
39%|███████████████▏ | 829k/2.13M [00:01<00:01, 912kB/s]
51%|██████████████████▉ | 1.09M/2.13M [00:01<00:00, 1.05MB/s]
63%|███████████████████████▏ | 1.34M/2.13M [00:01<00:00, 1.12MB/s]
74%|███████████████████████████▏ | 1.56M/2.13M [00:01<00:00, 1.13MB/s]
82%|██████████████████████████████▎ | 1.74M/2.13M [00:01<00:00, 1.07MB/s]
94%|██████████████████████████████████▌ | 1.99M/2.13M [00:02<00:00, 1.13MB/s]
0%| | 0.00/2.13M [00:00<?, ?B/s]
100%|█████████████████████████████████████| 2.13M/2.13M [00:00<00:00, 7.85GB/s]
Kalunga2016-WithinSession: 100%|██████████| 2/2 [00:23<00:00, 11.90s/it]
Kalunga2016-WithinSession: 100%|██████████| 2/2 [00:23<00:00, 11.55s/it]
score time ... pipeline codecarbon_task_name
0 0.333333 0.001147 ... CCA 9b6ff55e-36e1-4f27-adc0-bacbc643d908
1 0.335556 0.001058 ... CCA b1a89f36-4794-44e5-a3f0-9cfb3fa815ba
[2 rows x 11 columns]
Plot Results#
Here we plot the results, indicating the score for each subject
plt.figure()
sns.barplot(data=results, y="score", x="session", hue="subject", palette="viridis")
<Axes: xlabel='session', ylabel='score'>
And the computation time in seconds
plt.figure()
ax = sns.barplot(data=results, y="time", x="session", hue="subject", palette="Reds")
ax.set_ylabel("Time (s)")
plt.show()
Total running time of the script: (0 minutes 25.518 seconds)
Estimated memory usage: 322 MB
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